One challenge to understanding mechanisms of behavior change (MOBC) completely among individuals with alcohol use disorder (AUD) is that processes of change are theorized to be complex, dynamic (time varying), and at times non-linear, and they interact with each other to influence alcohol consumption. We used dynamical systems modeling to better understand MOBC within a cohort of problem drinkers undergoing treatment. We fit a mathematical model to ecological momentary assessment data from individual patients who successfully reduced their drinking by the end of the treatment. The model solutions agreed with the trend of the data reasonably well, suggesting the cohort patients have similar MOBC. This work demonstrates using a personalized approach to psychological research, which complements standard statistical approaches that are often applied at the population level. Our efforts collaborative efforts with our group (Kidist Bekele-Maxwell, R. A. Everett, Lyric Stephenson) and psychologists Alexis Kuerbis (Hunter College, CUNY) and Sijing Shao and Jon Morgenstern (Northwell Health).